Bioc2021: Visualisation of highly-multiplexed imaging data in R

Nils Eling1,2*, Nicolas Damond1,2, Tobias Hoch1,2 and Bernd Bodenmiller1,2

1Department for Quantitative Biomedicine, University of Zurich
2Institute for Molecular Health Sciences, ETH Zurich

*nils.eling@dqbm.uzh.ch

2 August 2021

Abstract

Highly multiplexed imaging acquires the single-cell expression of RNA, metabolites or proteins in a spatially-resolved fashion. These measurements can be visualized across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualized on segmented cell areas. This workflow describes the use of the cytomapper Bioconductor package to demonstrate functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging. The main functions of this package allow 1. the visualization of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

@nilseling
@NilsEling

1 Data and code availability

To follow this tutorial, please visit https://github.com/BodenmillerGroup/cytomapper_demos/tree/main/docs. The compiled .html of this workshop is hosted at: https://bodenmillergroup.github.io/cytomapper_demos. The cytomapper package can be installed via:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("cytomapper")

To reproduce the analysis, clone the repository:

git clone https://github.com/BodenmillerGroup/cytomapper_demos.git

and open the Bioc2021_workshop.Rmd file in the docs folder.

We provide three images, their segmentation masks and the quantified single-cell data in form of a SingleCellExperiment object in the data folder. The data is taken from A map of human type 1 diabetes progression by imaging mass cytometry.

2 Introduction

The analysis and visualization of highly-multiplexed imaging data relies on three types of data structures:

Multi-channel images realized as three dimensional arrays or dimensions x, y, c where the numeric entry of each voxel represents the intensity of the pixel at position x and y for channel c.
Single-channel segmentation masks realized as matrices where sets of pixels with the same integer ID represent individual objects (here, these are segmented cells)
A table containing the quantified features per cell and channel (e.g. mean pixel intensity)

The cytomapper package handles these data types using objects of the following S4 classes:

Multiple multi-channel images are stored in form of EBImage::Image objects within a cytomapper::CytoImageList object
Multiple single-channel segmentation masks are stored in form of EBImage::Image objects within a cytomapper::CytoImageList object
Per-cell intensity measures and cell-/channel-specific metadata are stored in a SingleCellExperiment::SingleCellExperiment container

cytomapper overview figure. A) The plotCells function combines a SingleCellExperiment and CytoImageList object to visualize marker expression or cell-specific metadata on segmentation masks. B) The plotPixels function requires a CytoImageList object to visualize the combined expression of up to six markers as composite images

The cytomapper package contains three broad functionalities:

Visualization of pixel-intensities as composites of up to six channels (plotPixels)
Visualization of cell-specific features on segmentation masks (plotCells)
Interactive gating of cells and visualization of gated cells on images (cytomapperShiny)

The follwing demonstration gives an overview on data handling and processing, data visualization and interactive analyses.

We use imaging mass cytometry data to highlight the functionality of the cytomapper package. However, any imaging technology is supported as long as the data can be read into R (memory restrictions and file type restrictions.)

The raw data has been processed using the ImcSegmentationPipeline. In this tutorial, we assume that image segmentation has been performed beforehand.

3 Reading in the data

The following section describes how to read in images (e.g. in .tiff format) into CytoImageList objects and how to generate SingleCellExperiment objects from these images.

3.1 Reading in images

The cytomapper::loadImages function reads in multi-channel images and segmentation masks into CytoImageList objects.

library(cytomapper)

# Read in 32-bit multi-channel images
(images <- loadImages("../data/images/", pattern = ".tiff"))

## CytoImageList containing 3 image(s)
## names(3): E30_a0_full_clean G23_a0_full_clean J01_a0_full_clean 
## Each image contains 38 channel(s)

# Read in 16-bit unsigned integer segmentation masks
(masks <- loadImages("../data/masks/", pattern = ".tiff"))

## CytoImageList containing 3 image(s)
## names(3): E30_a0_full_mask G23_a0_full_mask J01_a0_full_mask 
## Each image contains 1 channel

It is always recommended to observe the numeric pixel values to make sure that images were read in correctly:

# multi-channel images - first image, first channel
hist(log10(images[[1]][,,1] + 1))

# Segmentation mask
masks[[1]]

## Image 
##   colorMode    : Grayscale 
##   storage.mode : double 
##   dim          : 390 452 
##   frames.total : 1 
##   frames.render: 1 
## 
## imageData(object)[1:5,1:6]
##              [,1]         [,2]         [,3]         [,4]         [,5]
## [1,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.577707e-05
## [2,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.577707e-05
## [3,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
## [4,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
## [5,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
##              [,6]
## [1,] 0.000000e+00
## [2,] 0.000000e+00
## [3,] 4.577707e-05
## [4,] 4.577707e-05
## [5,] 4.577707e-05

We notice, that the segmentation masks were not read in as integer images. This behavior arises due to issues with accessing the tiff metadata after pre-processing using CellProfiler.

In these cases, the cytomapper::scaleImages function can be used to rescale segmentation masks to only contain integer IDs. Here, the scaling factor is 2 ^ 16 - 1 = 65535 accounting for the 16-bit unsigned integer encoding.

masks <- scaleImages(masks, 2 ^ 16 - 1)

masks[[1]]

## Image 
##   colorMode    : Grayscale 
##   storage.mode : double 
##   dim          : 390 452 
##   frames.total : 1 
##   frames.render: 1 
## 
## imageData(object)[1:5,1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    3    0
## [2,]    0    0    0    0    3    0
## [3,]    3    3    3    3    3    3
## [4,]    3    3    3    3    3    3
## [5,]    3    3    3    3    3    3

As an alternative, while reading in the images, the as.is option can be set to TRUE.

masks <- loadImages("../data/masks/", pattern = ".tiff", as.is = TRUE)

masks[[1]]

## Image 
##   colorMode    : Grayscale 
##   storage.mode : integer 
##   dim          : 390 452 
##   frames.total : 1 
##   frames.render: 1 
## 
## imageData(object)[1:5,1:6]
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    0    0    0    0    3    0
## [2,]    0    0    0    0    3    0
## [3,]    3    3    3    3    3    3
## [4,]    3    3    3    3    3    3
## [5,]    3    3    3    3    3    3

We can already visualize the segmentation mask to get an idea of the tissue structure:

plotCells(masks)

3.1.1 Reading data to disk

To increase scalability of the cytomapper package, images can be stored on disk making use of the HDF5Array and DelayedArray package. Reading in the data can also be parallelised, which is only recommended for large images.

format(object.size(images), units = "Kb")

## [1] "170235.4 Kb"

images_ondisk <- loadImages("../data/images/", pattern = ".tiff", 
                            on_disk = TRUE, h5FilesPath = "../data/images",
                            BPPARAM = BiocParallel::bpparam())

format(object.size(images_ondisk), units = "Kb")

## [1] "10.1 Kb"

All cytomapper functions support images and masks stored on disk.

3.2 The `SingleCellExperiment` object

In the original analysis of this dataset, single-cell features have been extracted using CellProfiler. For this tutorial, we provide a SingleCellExperiment object, which already contains the mean intensities per cell and channel and all relevant metadata (e.g. cell-type annotation).

The SingleCellExperiment can be read-in from the data folder:

(sce <- readRDS("../data/sce.rds"))

## class: SingleCellExperiment 
## dim: 38 5819 
## metadata(0):
## assays(2): counts exprs
## rownames(38): H3 SMA ... Ir191 Ir193
## rowData names(15): TubeNb MetalTag ... miCAT2 miCAT
## colnames(5819): E30_1 E30_2 ... J01_2173 J01_2174
## colData names(26): slide id ... Ethnicity BMI
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

colData(sce)[,c("ImageName", "CellNumber", "CellType")]

## DataFrame with 5819 rows and 3 columns
##            ImageName CellNumber    CellType
##          <character>  <integer> <character>
## E30_1            E30          1      ductal
## E30_2            E30          2  macrophage
## E30_3            E30          3      acinar
## E30_4            E30          4      acinar
## E30_5            E30          5      ductal
## ...              ...        ...         ...
## J01_2170         J01       2170      ductal
## J01_2171         J01       2171      acinar
## J01_2172         J01       2172      acinar
## J01_2173         J01       2173      acinar
## J01_2174         J01       2174      acinar

4 Image pre-processing

The following section will discuss setting image metadata and image normalization.

4.1 Setting metadata

Before image visualization, there are a few metadata entries that need to be set.

We will need to set the channel names of the images via the channelNames getter/setter function. The channel order here is the same as the row order of the SingleCellExperiment object. We will also need to synchronise the image IDs across the multi-channel images and segmentation masks by storing a DataFrame in the elementMetadata slot of the CytoImageList object.

# Add channel names
channelNames(images) <- rownames(sce)

# Add image name to metadata
(mcols(images) <- mcols(masks) <- DataFrame(ImageName = c("E30", "G23", "J01")))

## DataFrame with 3 rows and 1 column
##     ImageName
##   <character>
## 1         E30
## 2         G23
## 3         J01

4.2 Normalization

Channel normalization is a crucial step for image visualization to enhance the visibility of biological signals. One option is to transform (e.g. sqrt) the images. A more widely used alternative is to perform a range-scaling and clipping of channel intensities.

The cytomapper package exports the normalize function that scales channels between 0 and 1 across all images (by default). Clipping is performed by setting the inputRange parameter in the normalize function.

# Min-max normalization
images_norm <- normalize(images)

# Clip the images
images_norm <- normalize(images_norm, inputRange = c(0, 0.2))

4.3 Measuring object features

The cytomapper::measureObjects function takes the segmentation masks and multi- channel image objects and computes morphological (e.g. cell shape, size and location) and intensity features (default: mean intensity per channel and object/cell).

sce_measured <- measureObjects(mask = masks, image = images, 
                               img_id = "ImageName")
sce_measured

## class: SingleCellExperiment 
## dim: 38 5819 
## metadata(0):
## assays(1): counts
## rownames(38): H3 SMA ... Ir191 Ir193
## rowData names(0):
## colnames: NULL
## colData names(8): ImageName object_id ... m.majoraxis m.eccentricity
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

The pixel intensities per cell can be summarized in different ways (e.g. as quantiles). Furthermore, parallelization is possible by setting BPPARAM = bpparam(). Cell-specific morphological features are stored in colData(sce) while the mean pixel intensities per cell and channel are stored in counts(sce).

colData(sce_measured)

## DataFrame with 5819 rows and 8 columns
##        ImageName object_id    s.area s.radius.mean      m.cx      m.cy
##      <character> <numeric> <numeric>     <numeric> <numeric> <numeric>
## 1            E30         1         8      1.132373   172.375   1.62500
## 2            E30         2         5      0.969029   150.400   2.60000
## 3            E30         3        85      4.928794     7.400   4.36471
## 4            E30         4        41      3.148247   221.756   3.85366
## 5            E30         5        44      3.454595   190.523   5.95455
## ...          ...       ...       ...           ...       ...       ...
## 5815         J01      2170        40       3.15461   436.950   454.050
## 5816         J01      2171        64       4.26023   457.469   453.688
## 5817         J01      2172        42       3.51294   341.643   454.643
## 5818         J01      2173        10       1.55575    20.900   455.500
## 5819         J01      2174        16       2.46488    44.250   455.625
##      m.majoraxis m.eccentricity
##        <numeric>      <numeric>
## 1        3.96961       0.713215
## 2        4.34170       0.955796
## 3       13.40271       0.767751
## 4        8.17150       0.515845
## 5       10.23176       0.792695
## ...          ...            ...
## 5815     8.85424       0.722069
## 5816    12.40952       0.812136
## 5817    12.96285       0.936558
## 5818     6.43099       0.928765
## 5819    10.74725       0.985845

counts(sce_measured)[1:10, 1:10]

##              [,1]      [,2]        [,3]      [,4]        [,5]       [,6]
## H3     22.6329811 2.4361224 13.08346010 6.7423978 14.72265212 11.5127982
## SMA     0.7880026 1.0405174  1.26743278 0.9876558  0.32023911  0.7070429
## INS     0.0000000 0.0000000  0.24131852 0.2329416  0.16629238  0.2158641
## CD38    0.3090078 0.2992626  0.32912218 0.1752310  0.20383297  0.2546964
## CD44    2.3301406 0.4281695  0.59169109 0.4615597  1.78500872  0.4622892
## PCSK2   1.1045258 1.0593891  0.56905349 0.5634079  0.85554019  0.7053719
## CD99    0.3115875 0.3936061  0.77623081 1.0573374  0.89659225  0.9886102
## CD68    0.0000000 4.7920089  0.31981109 0.4370047  1.25933963  0.2232831
## MPO     0.0000000 5.1032834  0.07681261 0.1129901  0.04637183  0.1006678
## SLC2A1  2.8200562 2.2636643  0.79776477 1.4814364  1.74246915  0.4607339
##              [,7]      [,8]      [,9]      [,10]
## H3     6.65392760 8.7451991 6.6297040 9.66234309
## SMA    1.94122101 1.2825997 1.3894975 0.78360098
## INS    0.09161043 0.1462787 0.1709020 0.09961374
## CD38   0.26884190 0.5762133 0.3820182 0.50709601
## CD44   0.40098510 0.5384129 0.3380702 0.75498592
## PCSK2  0.65091868 0.8107124 1.0038303 0.68700536
## CD99   0.73302101 0.7973502 0.5520242 0.94204760
## CD68   1.04103449 0.7391340 0.2099665 1.36866162
## MPO    3.35050052 5.6631805 0.6724382 8.20452772
## SLC2A1 1.61920737 2.3110813 1.0785549 2.68912075

5 Data visualization

After having formatted the data, we can now move on to data visualization.

5.1 Visualizing pixel intensities

The cytomapper::plotPixels function is the main function for visualization of pixel intensities.

The easiest way of visualizing images is to just specify channel names:

plotPixels(images, 
           colour_by = c("PIN", "CDH"))

Here, the intensities appear weak. For this, we have normalized the images:

plotPixels(images_norm, 
           colour_by = c("PIN", "CDH"))

We can see a decline in pro-insulin expression while E-cadherin expression stays the same.

Instead of normalizing the images, you can also specify the background (b), contrast (c) and gamma (g) levels of the images:

plotPixels(images, 
           colour_by = c("PIN", "CDH"),
           bcg = list(PIN = c(0, 5, 1),
                      CDH = c(0, 5, 1)))

The cytomapper package allows flexible adjustments of all visual attributes of the images:

plotPixels(
    image = images,
    colour_by = c("PIN", "CD4", "CD8a"), 
    colour = list(PIN = c("black", "yellow"),
                  CD4 = c("black", "blue"),
                  CD8a = c("black", "red")),
    bcg = list(PIN = c(0, 10, 1),
               CD4 = c(0, 8, 1),
               CD8a = c(0, 10, 1)),
    image_title = list(
        text = c("Non-diabetic",
                 "Recent onset T1D",
                 "Long duration T1D")
        ),
    scale_bar = list(
        length = 100,
        label = expression("100 " ~ mu * "m")
        ))

The colour_by parameter defines the channel names by which to colour the composite. Per channel, a colour scale is generated by setting colour. The attributes of the image titles can be set via the parameter image_title and attributes of the scale bar are set via the parameter scale_bar.

To see all available parameter options, refer to ?plotting-param

5.2 Visualizing cells

In the next step, we can visualize mean pixel intensities and cell-specific metadata directly on the segmentation masks.

For this, we will use the cytomapper::plotCells function while combining the SingleCellExperiment object and the CytoImageList storing the segmentation masks:

plotCells(mask = masks, object = sce, 
          cell_id = "CellNumber", img_id = "ImageName",
          colour_by = c("PIN", "CD8a", "CD4"), 
          exprs_values = "exprs")

The assay(sce, "exprs") slot stores the asinh-transformed mean pixel intensities per channel and cell. The cell_id entry needs to be stored in the colData(sce) slot to link each individual cell to their corresponding pixels in the segmentation mask. The img_id parameter specifies the colData(sce) entry storing the name/ID of the image to which each cell belongs. This should also be an entry in mcols(masks).

The SingleCellExperiment object is subsettable to highlight only certain cells:

cur_sce <- sce[,sce$CellType %in% 
    c("beta", "alpha", "delta", "Tc", "Th")]

plotCells(mask = masks, object = cur_sce, 
          cell_id = "CellNumber", img_id = "ImageName",
          colour_by = c("PIN", "CD8a", "CD4"), 
          exprs_values = "exprs",
          missing_colour = "white")

Here, missing_colour defines the colour of cells missing in the SingleCellExperiment object.

This can also be useful when visualizing cell-specific metadata:

plotCells(
    mask = masks,
    object = cur_sce,
    cell_id = "CellNumber",
    img_id = "ImageName",
    colour_by = "CellType",
    image_title = list(
        text = c("Non-diabetic",
                 "Recent onset T1D",
                 "Long duration T1D"),
        colour = "black"),
    scale_bar = list(
        length = 100,
        label = expression("100 " ~ mu * "m"),
        colour = "black"),
    missing_colour = "white",
    background_colour = "gray")

5.3 Visualizing cells and pixel intensities

Finally, all three objects (multi-channel images, segmentation masks and the single-cell data) can be combined to outline cells on composite images.

As a first demonstration, we select only pancreatic beta cells and visualize their corresponing marker pro-insulin:

cur_sce <- sce[,sce$CellType == "beta"]

plotPixels(images_norm,
           mask = masks,
           object = cur_sce,
           cell_id = "CellNumber",
           img_id = "ImageName",
           colour_by = c("PIN", "H3"),
           bcg = list(H3 = c(0, 2, 1)),
           colour = list(PIN = c("black", "yellow"),
                         H3 = c("black", "blue"),
                         CellType = c(beta = "red")),
           outline_by = "CellType")

One can now also visualize multiple cell types and multiple markers together:

6 Interactive visualization

We have developed a shiny application to gate cells based on their mean pixel intensity with additional visualization on the images.

The idea behind this gating strategy is to generate ground truth cell type labels, which can be used to train a classifier and classify cells rather than relying on clustering.

The shiny app can be opened using the cytomapper::cytomapperShiny function:

if (interactive()) {
    
    cytomapperShiny(sce, mask = masks, image = images,
                    img_id = "ImageName", cell_id = "CellNumber")
    
}

Using the Shiny application

The help page provides a recommended workflow on how to most efficiently use the app. The workflow is solely a recommendation - the app provides full flexibility to change settings during each step. To see the full documentation, please refer to the help page found at ?cytomapperShiny

Select the number of plots The slider under “General controls” can be used to specify the number of plots on which to perform gating. Up to two markers can be visualized per plot.
Select the sample
The assay dropdown selection under “General controls” allows the user to specify on which assay entry to perform gating. In most cases, a log- or arcsinh-transformation can help to distinguish between ‘positive’ and ‘negative’ populations.
Select the markers
For each plot, up to two markers can be specified. If selecting a single marker, please specify this marker in the first of the two dropdown menus. A violin plot is used to visualize the expression of a single marker while a scatter plot is used to visualize the expression of two markers.
Gate cells
When selecting cells in one plot, only those cells are visualized on the following plot. Once markers, the assay or the number of plots are changed, gates are cleared.
Observe the selected cells
After gating, the selected cells are visualized on the corresponding images by switching to the “Images tab”. By default, the first marker is selected. The user can change the displayed marker or press reset marker to switch to the markers used for gating. If a multi-channel image object is provided, the contrast of the image can be changed. The right panel visualizes the selected cells either by filling in the segmentation masks or by outlining the cells on the images.
Change samples
Samples can now be iteratively changed using the dropdown menu under General controls . The gates will remain on the plots and can be adjusted for each sample.
Save the selected cells
Finally, the selected cells can be saved by clicking the download button next to the ‘?’ symbol. The selected cells will be stored as a SingleCellExperiment object in .rds format. Per selection, the user can provide a Cell label that will be stored in the colData under the cytomapper_CellLabel entry of the downloaded object.

7 Further resources

For pre-processing multiplexed imaging data, please refer to the ImcSegmentationPipeline and/or the steinbock package.

To test the cytomapper package on different datasets, check out the imcdatasets Bioconductor package.

The imcRtools package is currently being developed to facilitate handling of multiplexed imaging data and spatial analysis.

Session info

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] cytomapper_1.4.1            SingleCellExperiment_1.14.1
##  [3] SummarizedExperiment_1.22.0 Biobase_2.52.0             
##  [5] GenomicRanges_1.44.0        GenomeInfoDb_1.28.1        
##  [7] IRanges_2.26.0              S4Vectors_0.30.0           
##  [9] BiocGenerics_0.38.0         MatrixGenerics_1.4.0       
## [11] matrixStats_0.60.0          EBImage_4.34.0             
## [13] BiocStyle_2.20.2           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           fontawesome_0.2.2      RColorBrewer_1.1-2    
##  [4] tools_4.1.0            bslib_0.2.5.1          utf8_1.2.2            
##  [7] R6_2.5.0               svgPanZoom_0.3.4       HDF5Array_1.20.0      
## [10] vipor_0.4.5            DBI_1.1.1              colorspace_2.0-2      
## [13] rhdf5filters_1.4.0     raster_3.4-13          sp_1.4-5              
## [16] gridExtra_2.3          tidyselect_1.1.1       compiler_4.1.0        
## [19] DelayedArray_0.18.0    bookdown_0.22          sass_0.4.0            
## [22] scales_1.1.1           systemfonts_1.0.2      stringr_1.4.0         
## [25] digest_0.6.27          tiff_0.1-8             fftwtools_0.9-11      
## [28] rmarkdown_2.9          svglite_2.0.0          XVector_0.32.0        
## [31] jpeg_0.1-9             pkgconfig_2.0.3        htmltools_0.5.1.1     
## [34] highr_0.9              fastmap_1.1.0          htmlwidgets_1.5.3     
## [37] rlang_0.4.11           shiny_1.6.0            jquerylib_0.1.4       
## [40] generics_0.1.0         jsonlite_1.7.2         BiocParallel_1.26.1   
## [43] dplyr_1.0.7            RCurl_1.98-1.3         magrittr_2.0.1        
## [46] GenomeInfoDbData_1.2.6 Matrix_1.3-3           Rcpp_1.0.7            
## [49] ggbeeswarm_0.6.0       munsell_0.5.0          Rhdf5lib_1.14.2       
## [52] fansi_0.5.0            viridis_0.6.1          abind_1.4-5           
## [55] lifecycle_1.0.0        stringi_1.7.3          yaml_2.2.1            
## [58] zlibbioc_1.38.0        rhdf5_2.36.0           grid_4.1.0            
## [61] promises_1.2.0.1       shinydashboard_0.7.1   crayon_1.4.1          
## [64] lattice_0.20-44        magick_2.7.2           locfit_1.5-9.4        
## [67] knitr_1.33             pillar_1.6.1           codetools_0.2-18      
## [70] glue_1.4.2             evaluate_0.14          BiocManager_1.30.16   
## [73] png_0.1-7              vctrs_0.3.8            httpuv_1.6.1          
## [76] gtable_0.3.0           purrr_0.3.4            assertthat_0.2.1      
## [79] ggplot2_3.3.5          xfun_0.24              mime_0.11             
## [82] xtable_1.8-4           later_1.2.0            viridisLite_0.4.0     
## [85] tibble_3.1.3           beeswarm_0.4.0         ellipsis_0.3.2